{"id":"W4413802745","doi":"10.24908/iqurcp19061","title":"Engineering Hyperthermostable Nylonase, TvgC, to Improve Catalytic Efficiency for Degradation","year":2025,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Enzyme Catalysis and Immobilization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Degradation (telecommunications); Saturated mutagenesis; Nylon 6; Catalysis; Chemistry; Mutagenesis; Inert; Catalytic efficiency; Materials science; Chemical engineering; Biochemical engineering; Combinatorial chemistry; Computer science; Mutant; Biochemistry; Polymer; Organic chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009355105,0.0002038155,0.0002027716,0.0003433157,0.0002366564,0.0002396489,0.0004202547,0.0001554015,0.000003723329],"category_scores_gemma":[0.001420212,0.000200269,0.00007946954,0.0007876672,0.00009485817,0.00003333571,0.0002422495,0.0001696278,0.00001696198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080863,"about_ca_system_score_gemma":0.0003921905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006110089,"about_ca_topic_score_gemma":0.00001339132,"domain_scores_codex":[0.9980342,0.00001988635,0.0003071143,0.0007074285,0.0003312011,0.0006001361],"domain_scores_gemma":[0.9979194,0.00006175051,0.00005701081,0.0002707254,0.001542046,0.0001490367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001133997,0.00007926969,0.0002975117,0.000209385,0.00005513266,3.093266e-7,0.0001822867,0.00009289241,0.9716985,0.01367539,0.009358504,0.004237481],"study_design_scores_gemma":[0.0006452139,0.0004922266,0.0001837663,0.0001693815,0.00002752054,0.000001754733,0.0006367982,0.007370139,0.9549217,0.001739665,0.03347695,0.0003348942],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7530994,0.0003808823,0.2323881,0.008659397,0.0004072263,0.002603416,0.00004018464,0.00009377612,0.002327594],"genre_scores_gemma":[0.9919406,0.0001723017,0.0009751872,0.0001365228,0.0001428383,0.0004790961,0.0002069793,0.00003149942,0.005914969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2388412,"threshold_uncertainty_score":0.8166734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03724059341067929,"score_gpt":0.3350803672471686,"score_spread":0.2978397738364893,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}